In high-dimensional regression or classification frameworks, variable selection is a difficult task, that becomes even more challenging in the presence of highly correlated predictors [1]}. Most of the methods mentioned above can deal with high-dimensional data and find the important individual variables. They don't consider the interaction among input features. Our approach is an ensemble of models of critical subspaces, incorporating the interaction among variables for high-dimensional regression. The critical subspace originates from a randomized search and is verified by selection criterion. The approach will keeps adding critical subspaces until it meets the termination criterion.
